class_docstr: Base ModelInterface for torch and tensorflow models.
class_docstr_tok: ["Base","ModelInterface","for","torch","and","tensorflow","models","."]
docstr: In every iteration yields a copy of the model with one additional layer's parameters randomized.
  For cascading randomization, set order (str) to 'top_down'. For independent randomization,
  set it to 'independent'. For bottom-up order, set it to 'bottom_up'.
docstr_tok: ["In","every","iteration","yields","a","copy","of","the","model","with","one","additional","layer","'s","parameters","randomized",".","For","cascading","randomization",",","set","order","(","str",")","to","'top_down","'",".","For","independent","randomization",",","set","it","to","'independent","'",".","For","bottom-up","order",",","set","it","to","'bottom_up","'","."]
code: def get_random_layer_generator(
      self, order: str = "top_down", seed: int = 42
  ) -> Generator[Tuple[str, M], None, None]:
      """
      In every iteration yields a copy of the model with one additional layer's parameters randomized.
      For cascading randomization, set order (str) to 'top_down'. For independent randomization,
      set it to 'independent'. For bottom-up order, set it to 'bottom_up'.
      """
      raise NotImplementedError
  
file_path: Quantus/quantus/helpers/model/model_interface.py
imports: import warnings
  from importlib import util
  from abc import ABC, abstractmethod
  from typing import Any, Dict, Optional, Tuple, List, Union, Generator, TypeVar, Generic
  import numpy
label_desc: Use image node_id 7 for calling the ModelInterface obj's underlying member method code with example usage: obj.get_random_layer_generator(order, seed) without return types